Search Results (2,970)

Background: Preoperative screening for methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-susceptible S. aureus (MSSA) is intended to identify patients at increased risk of surgical site infection and guide decolonization and perioperative antimicrobial prophylaxis. However, many molecular assays reduce this decision to a binary positive/negative result, potentially obscuring clinically important distinctions in organism identity, methicillin resistance attribution, and mupirocin resistance risk. Methods: This structured narrative review organized direct perioperative evidence and indirect mechanistic, implementation, and economic evidence around one question: how MRSA/MSSA screening can move from organism detection to actionable molecular triage. Results: Useful preoperative reporting depends on assigning resistance markers to the correct organism. The proposed multi-target NAAT framework organizes concordant and discordant molecular patterns into provisional reportable categories, including MSSA, MRSA, methicillin-resistant non-aureus Staphylococcus/CoNS, mixed populations, SCCmec dropout patterns, mupirocin resistance marker states, and invalid or indeterminate results. No externally validated composite score, universal molecular cutoff, or prospectively validated target-to-action decision rule currently links all of these categories to specific perioperative actions. Conclusions: Preoperative MRSA/MSSA screening may benefit from moving beyond binary reporting, but the framework presented here is a development-stage rule set rather than a validated clinical decision instrument. Assay-specific analytical thresholds, locked target combination rules, and prospective clinical and implementation validation are required before the framework can be used to assign patients reproducibly to management pathways. Until such validation is completed, the proposed categories should be interpreted as a testable reporting and validation architecture rather than as universal prophylaxis or decolonization instructions. Full article

A consistent computational framework is presented that integrates Markov chain modeling, decision optimization, and cost–benefit analysis for the life-cycle management of engineering assets. The approach combines deterioration modeling with a complete economic evaluation and optimization of maintenance decisions. Each condition state of the system is associated with possible actions such as do-nothing, preventive maintenance, major repair, and replacement, each defined by its own transition matrix or generator describing state changes. The expected one-step reward is formulated as the difference between benefits and all relevant costs including operating, action, and failure costs. The optimization problem is expressed as a discounted Markov decision process and solved by linear programming. The resulting stationary policy specifies the optimal decision rule for every state. Both discrete-time and continuous-time variants are implemented. Transition matrices and generator matrices are linked using a matrix exponential mapping for the selected step length. Under state-dependent policies, the discrete step model and the continuous-time feedback model may lead to different long-run state shares. This is caused by different decision timing. The continuous-time variant also provides reliability indicators such as survival and hazard. It can also provide mean time to absorption under an absorbing failure interpretation. Simulation under the optimal policy yields state trajectories, present values of benefits and costs, and key economic indicators such as net present value, benefit–cost ratio, equivalent annual cost, and equivalent annual net benefit. The framework forms a unified and practical tool that connects reliability analysis, Markov optimization, and life-cycle cost–benefit evaluation for long-term infrastructure management. Full article

An automated cartridge-based Vcheck M Canine Vector 8 Panel for qualitative detection of Leishmania spp. DNA in canine lymph node aspirates, an off-label specimen type, using laboratory qPCR as the comparator, was evaluated. Fifty-seven residual lymph node aspirate suspensions from dogs investigated for suspected canine leishmaniosis (CanL) were tested. Reference qPCR detected L. infantum DNA in 29 samples. Vcheck M was positive in 22/29 qPCR-positive samples and negative in 28/28 qPCR-negative samples, corresponding to positive percent agreement/sensitivity of 75.9% (95% CI, 56.5–89.7) and negative percent agreement/specificity of 100.0% (95% CI, 87.7–100.0). Agreement was substantial (Cohen’s kappa, 0.76), and discordance was asymmetric (McNemar p = 0.016). Vcheck-negative/qPCR-positive results were mainly observed at low qPCR parasite loads: 6/7 discordant samples contained ≤30 parasites/mL, whereas all samples with ≥500 parasites/mL were Vcheck positive. Among Vcheck-positive clinical samples, Vcheck Ct correlated inversely with log10 qPCR parasite load (Spearman rho = −0.77; p < 0.001). In a single-run dilution series, the lowest instrument-positive L. infantum standard was 10³ parasites/mL. Purified L. major, L. braziliensis, and L. tropica DNA were also detected. Vcheck M showed high specificity as a rapid rule-in test for Leishmania spp. detection in canine lymph node aspirates. However, negative results should not exclude infection in symptomatic or strongly suspected dogs and should be confirmed by qPCR, particularly when low parasite burden is plausible or when the result is critical for diagnostic or therapeutic decision-making. Full article

Wind power producers face increasing market risks in electricity spot markets because output uncertainty may lead to large imbalance penalties and unstable revenues. This study aims to improve the market participation performance of wind farm consortia by coordinating day-ahead bidding, real-time deviation correction, and internal revenue allocation. The main novelty of this study is the integration of consortium-level bidding, shared energy storage leasing, and post-settlement revenue-cost allocation within a unified decision-allocation framework. A two-stage coordinated bidding model is developed for a wind farm consortium that leases shared energy storage to mitigate real-time power deviations. A Shapley value-based allocation mechanism is further introduced to distribute consortium revenue, while the shared energy storage leasing cost is allocated using an additional revenue-proportional fairness rule. Case studies show that the proposed strategy can reduce deviation penalties, increase the final net revenue after leasing cost, and maintain fair incentives among consortium members. Sensitivity analyses further demonstrate that the economic performance of the consortium is affected by storage size, charging/discharging efficiency, and wind farm output correlation. The proposed framework provides a practical decision-making reference for wind power aggregation, shared energy storage utilization, and coordinated participation in electricity spot markets. Full article

Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, driven by the increasing availability of large-scale clinical data and advances in machine learning. Early computational applications in cardiology were primarily limited to rule-based electrocardiogram interpretation systems. Over time, these approaches have evolved into sophisticated deep learning models capable of analysing complex cardiovascular signals and imaging data. In parallel with the broader development of digital health technologies, including wearable devices, electronic health records, and remote monitoring systems, AI applications have expanded across multiple domains of cardiovascular care. These now include electrocardiographic (ECG) and electrophysiological analysis, cardiovascular imaging, surgical planning, and multimodal risk prediction. More recently, multimodal AI models have emerged that integrate heterogeneous data sources such as imaging, physiological signals, clinical records, and genomic information, enabling more comprehensive characterisation of cardiovascular disease. Beyond diagnostic applications, AI is increasingly influencing system-level aspects of cardiovascular medicine, including clinical decision support, workflow optimisation, medical education, and clinical trial design. This narrative review traces the historical and clinical evolution of artificial intelligence in cardiovascular medicine from early automated ECG interpretation systems to contemporary multimodal and system-level applications. It highlights key technological developments, current clinical applications, translational challenges, and the emerging role of AI within digital cardiovascular health ecosystems, with particular emphasis on early disease detection, risk stratification, prognostic modelling, and personalised cardiovascular care. Full article

Green low-carbon data centers operate as coupled cyber-energy systems whose dispatch must coordinate renewable generation, grid exchange, battery storage, cooling load, flexible computing workload, carbon-intensity signals, and reliability constraints. This study develops and evaluates a safety-constrained deep reinforcement learning framework for source–load–storage coordinated operation of a grid-connected green data center. The operating problem is formulated as a constrained Markov decision process with state variables describing the IT load, deferrable workload backlog, renewable availability, electricity price, marginal carbon intensity, battery state of charge, server-room temperature, reserve margin, and calendar context. The action space covers grid import and export, renewable utilization, storage charge and discharge, workload shifting, and cooling control. The learning architecture combines a constrained actor–critic policy, adaptive Lagrangian safety critics, and a control barrier function (CBF)-based action shield that projects unsafe actions onto an explicitly defined operating set before plant execution. The shield is specified as a low-dimensional quadratic projection over state-dependent SOC, thermal, reserve, SLA, and grid-interface constraints, while cumulative risks are priced through Lagrangian safety budgets during policy training. The evaluation uses a controlled and auditable benchmark simulation with normalized public-data-compatible profiles, declared scenarios, random seeds, neural-network settings, and mechanism-matched baselines; it is not a telemetry-based verification or hardware certification of a deployed data center. Within this declared benchmark, the proposed safe DRL controller produces a simulated 13.1% emission reduction relative to the Rule-based controller, 95.8% renewable utilization, a normalized annual cost of 0.91, and fewer boundary contacts than the tested unconstrained, Lagrangian-only, and shield-only PPO variants. These percentages are simulator outputs relative to the stated benchmark and must not be interpreted as measured field savings. The results show how separating reward learning, cumulative safety pricing, and one-step engineering projection changes low-carbon dispatch within the specified model. Full article

Mobile platforms such as high-speed trains and unmanned aerial vehicles (UAVs) experience quasi-periodic variation in link quality as they move through a cellular base-station lattice, so the service rate of their on-board uplink buffer is itself time-periodic. We model this buffer as a periodic $M/M\left(t\right)/1$ queue whose service rate follows from a signal-to-noise-ratio (SNR)-to-rate map and construct an edge-resident controller that exploits this periodic structure for real-time quality-of-service (QoS) control. From a harmonic-balance (Fourier–Galerkin) solution of the periodic regime, the controller derives backlog and tail-probability indicators and uses them to drive admission, redundancy and handover decisions on the device. The method rests on a stability criterion and a quantitative error bound for the spectral truncation, under stated regularity and stability conditions, and is validated against Monte Carlo simulation along a ∼650 km geo-anchored corridor: on the periodic backbone, the solver matches simulation to within about $1.6\%$, and a coefficient-driven admission rule lowers the 99th-percentile delay by about $28\%$ relative to a reactive baseline at high load. On the full map-derived profile with aperiodic coverage gaps, the proposed proactive controller—spectral backbone admission combined with a radio-map look-ahead—attains the lowest mean and tail delay, about $27\%$ and $21\%$ below the reactive baseline and $54\%$ and $42\%$ below uncontrolled DropTail, with buffer overflow cut from $2.2\%$ to $0.1\%$, at a deliberate admitted-load cost (goodput ≈0.84 vs. $0.94$). An operation-count analysis indicates compatibility with sub-$100$ms control deadlines on a Cortex-A55-class system-on-chip. The controller runs on the device itself, without cloud or GPU, and the architecture is realised in a granted patent; end-to-end hardware benchmarking and an extension to non-Poisson traffic are left for future work. Full article

Non-obstructive azoospermia (NOA) is the most severe phenotype of male-factor infertility and reflects impaired spermatogenesis rather than ductal obstruction. Microdissection testicular sperm extraction (micro-TESE) is the primary sperm retrieval method, but sperm retrieval rates remain at approximately 40–60% and vary with etiology, genetics, histopathology, surgical expertise, and endocrine phenotype. This narrative review synthesizes major international and regional guidelines and contemporary evidence on preoperative hormonal optimization, with particular emphasis on endogenous testosterone dynamics. Exogenous testosterone is contraindicated in fertility-seeking men because it suppresses gonadotropins and intratesticular testosterone. By contrast, selective estrogen receptor modulators, aromatase inhibitors, human chorionic gonadotropin, and follicle-stimulating hormone aim to preserve or augment endogenous Leydig- and Sertoli-cell function. Low-certainty, predominantly observational evidence suggests an association between hormonal pretreatment and higher sperm retrieval in selected normogonadotropic or hypogonadal men, but not consistently in hypergonadotropic NOA. A larger testosterone rise during stimulation has been positively associated with retrieval and may reflect residual Leydig-cell reserve, although causality and transferable thresholds remain unproven. Preoperative endocrine therapy should therefore remain individualized, phenotype-guided, off-label, closely monitored, and preferably investigated within clinical trials; the testosterone response is best regarded as a candidate prognostic biomarker rather than a validated therapeutic target or decision rule. Full article

Conventional DC control and protection (C&P) systems rely on single-modal electrical data and are susceptible to false tripping and missed detection under complex operating conditions or novel fault types. In high-voltage direct current (HVDC) transmission, multi-modal data encompass time-series electrical quantities, unstructured transient waveforms, condition monitoring measurements, and environmental variables, each reflecting the system operating state from a distinct dimension with significant inter-modal complementarity. Nevertheless, fusing these heterogeneous modalities poses three key challenges: feature conflicts arising from data heterogeneity, difficulty embedding domain-specific C&P knowledge into data-driven models, and degraded model robustness under data noise and missing data conditions. This paper proposes a three-layer hierarchical fusion model that integrates multimodal data preprocessing, a C&P phase-aware rule-guided feature weighting strategy, and a dual-path decision mechanism. Experiments conducted on a dataset covering normal operation, typical fault, and complex operating scenarios demonstrate that the proposed model achieves an overall fault identification accuracy of 96.2%, which is 13.9 and 6.5 percentage points higher than a single-modal baseline and a generic multimodal model, respectively. The average decision latency of 7.2 ms satisfies the millisecond-level real-time requirement of industrial C&P systems, confirming the engineering applicability of the proposed approach. Full article

Coral reefs are typical ecosystems of high diversity and productivity, but they are facing significant degradation in their structure and function due to the dual stresses of climate change and human activities. To uncover the trophic structure, energy flow, and environmental driving mechanisms of nearshore coral reefs in Hainan, this study constructed mass balance models for five regions, Sanya, Changjiang, Lingao, Wenchang, and Wanning, using Ecopath with Ecosim, and conducted quantitative analysis in conjunction with satellite environmental monitoring data. The results showed that the trophic levels of functional groups in different ecosystems ranged from 1.00 to 5.00, with Sanya and Changjiang having the highest trophic levels (4.505, 4.656), higher than Lingao, Wenchang, and Wanning (3.776–3.924). The system average trophic transfer efficiency ranged from 15.98% to 30.25%, generally higher than the classic Lindeman 10% rule, with Changjiang being the highest (30.25%) and Lingao the lowest (15.98%). In terms of material cycling, the energy utilization efficiency of primary trophic levels was low, with a large amount of energy retained as detritus at lower trophic levels; Sanya and Changjiang showed more complete detritus chain energy cycling, with Finn cycling indices reaching 7.69 and 2.53, respectively, indicating higher maturity. Keystone analysis identified zooplankton, corals, and medium carnivorous fish as key functional groups with a decisive impact on system stability. Environmental correlation analysis revealed that particulate inorganic carbon (Pic), light diffuse attenuation coefficient (Kd), chlorophyll-a concentration (Chl_a), and sea surface temperature (Sst) were the main regulating factors, among which Pic showed a significant peaked relationship with the total system throughput, total biomass, and total production, with an optimal range of 0.0050–0.0075 mol/m². Overall, the material cycling and energy flow states of the Sanya and Changjiang coral reef ecosystems were stronger than those of Lingao, Wenchang, and Wanning, with lower fishing pressure in the former contributing to a more complex food web and enhanced community structural stability. This study provides the first systematic quantitative assessment of food-web structures across five distinct nearshore coral reef regions in Hainan, introducing an early-warning threshold for particulate inorganic carbon that offers direct, actionable reference for regional ecosystem-based management. Full article

Toll-road operators must coordinate pavement rehabilitation and toll pricing because surface condition and tolls jointly affect route choice, realized demand, deterioration, and long-term revenue. Motivated by infrastructure REIT asset-operation requirements, this study develops a stochastic bi-level multi-period model for joint pavement maintenance and toll pricing under demand feedback. The upper level selects annual tolls and rehabilitation intensities for tolled links subject to budget and service constraints. The lower level solves an elastic-demand user equilibrium based on generalized travel disutility. The operator objective extends discounted-profit maximization by adding revenue coefficient of variation, maximum drawdown, and terminal pavement value. Budget availability and deterioration uncertainty are represented by scenario multipliers, and the model is solved by a real-coded genetic algorithm coupled with the method of successive averages (GA-MSA). Experiments on the Li-Sheng benchmark and a semi-empirical Nanjing toll-road REIT corridor show that stochastic coordinated decisions retain more than 97% of the NPV achieved by the GA-MSA profit-oriented benchmark while improving revenue stability and limiting downside risk. Supplementary comparisons with PSO-MSA and DE-MSA show that alternative upper-level search rules identify different points on the normalized risk–return surface. The findings support treating maintenance and pricing as an integrated asset-operation problem for long-horizon toll-road assets. Full article

This critical perspective review first classifies glyphosate-sensing platforms and then evaluates their analytical applicability to agricultural runoff. Platforms are divided at the primary level into optical and electrochemical transduction, because these families measure different physical signals and have different sources of matrix interference. They are then grouped by the process that produces selectivity or signal change: direct interaction or metal coordination, affinity recognition by aptamers, antibodies, or molecularly imprinted polymers, catalytic modulation by enzymes or nanozymes, and separation or preconcentration before detection. This hierarchy distinguishes recognition chemistry from transduction method and device configuration. The review next defines four intended analytical applications—trace surveillance, runoff event screening, spill triage, and laboratory-adjacent confirmation—and compares them in terms of matrix, target concentration range, sample preparation, reporting metrics, and quality control requirements. Glyphosate occurs in dissolved and particle-associated forms, degrades mainly to AMPA, and coexists with phosphate, glufosinate, divalent cations, natural organic matter, and suspended sediment. Consequently, the lowest reported LOD is rarely the sole criterion for selecting a method. Matrix-matched calibration, spike recovery, selectivity, response time, storage stability, reader requirements, and invalid result rules determine whether an assay is suitable for a specified analytical application. The most defensible near-term approach combines matrix-specific sample preparation, platform-specific controls, and LC-MS/MS confirmation when results are regulatory, contested, or close to a decision threshold. Full article

An Environmental Impact Assessment (EIA) is a legal obligation in Bangladesh for projects with substantial environmental and socio-economic impacts. Over time, Bangladesh has established sound policies, rules, and a growing workforce to support the EIA process. However, two key issues persist: the effectiveness of EIA reports and weak implementation of Environmental Management Plans (EMPs). In practice, implementing agencies often prioritize physical progress over environmental safeguards, treating the EIA as merely a clearance formality for Planning Commission approval. Regulatory bodies, such as the Department of Environment and the Planning Commission, frequently neglect the substantive review of EIA reports, focusing on checklist requirements rather than rigorous environmental scrutiny. This procedural mindset undermines the role of EIA as a strategic planning and decision-making tool. This study examines each step of the EIA process, from report preparation to EMP implementation, highlighting gaps that require sustainable improvements. A mixed-method approach was employed to evaluate the quality of the EIA process, with particular attention to its methodological rigor, accuracy of impact assessment, challenges in public participation, and the effectiveness of the proposed EMPs and their implementation. The recommendations presented in this study aim to enhance the efficiency of the EIA process and promote its application in similar contexts across developing countries. Full article

This research focuses on the creation and implementation of a digital Decision Support System for building inspections at the Bureau of Fire Protection (BFP) in Romblon, Romblon. For a long period, the agency has relied on traditional, paper-heavy workflows, leading to operational bottlenecks that necessitate modernization. This project re-imagines BFP operations by introducing a web-based framework to handle building permit and certificate applications digitally. The paper outlines the technical parameters, data structures, and configurations used to capture inspection details essential for generating official reports and Fire Safety Inspection Certificates (FSICs). Key findings highlight the success of contactless licensing procedures and the automated generation of corrective actions based on the Revised Implementing Rules and Regulations of the Philippine Fire Code (RA 9514). Evaluation via the ISO 25010:2011 metric yielded a weighted mean of 4.41, signifying a “very satisfactory” performance level for the system within the Romblon BFP office. Full article

Artificial intelligence is increasingly integrated into physical products, industrial scenarios, and innovation ecosystems, yet management research has focused mainly on AI adoption rather than the depth of organizational integration. This study examines how AI embedding affects enterprise competitiveness in China’s embodied intelligence industry and whether competitive structure reconfiguration mediates this relationship. Using survey data from 266 firms and partial least squares structural equation modeling, the analysis shows that AI embedding positively affects both enterprise competitiveness and competitive structure reconfiguration. Competitive structure reconfiguration also improves enterprise competitiveness and partially mediates the focal relationship, with a variance accounted for value of 43.7%. By contrast, the moderating effects of data–computing foundation and scenario openness are not supported. These findings indicate that the competitive value of AI depends not only on adoption but also on its integration into R&D, decision-making, organizational coordination, and scenario development, as well as on the structural changes that follow. The study contributes by distinguishing AI embedding from AI adoption and by identifying competitive structure reconfiguration as a process mechanism linking embedded AI to technological, ecosystem, and rule-based competitiveness. Full article